
# Installing the libraries with the specified version.
# uncomment and run the following line if Google Colab is being used
# !pip install scikit-learn==1.2.2 seaborn==0.13.1 matplotlib==3.7.1 numpy==1.25.2 pandas==1.5.3 imbalanced-learn==0.10.1 xgboost==2.0.3 -q --

# Data manipulation
import numpy as np
import pandas as pd
import seaborn as sns

# Visualization
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go

# remove warning errors
import warnings
warnings.filterwarnings('ignore')

# Scikit learn
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, confusion_matrix, classification_report
from sklearn.pipeline import Pipeline

# SMOTE
from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import RandomUnderSampler

# To help with model building
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import (
    AdaBoostClassifier,
    GradientBoostingClassifier,
    RandomForestClassifier,
    BaggingClassifier,
)

from xgboost import XGBClassifier
from sklearn.linear_model import LogisticRegression

# To suppress scientific notations
pd.set_option("display.float_format", lambda x: "%.3f" % x)

# To supress warnings
import warnings

warnings.filterwarnings("ignore")


# Installing the libraries with the specified version.
# uncomment and run the following lines if Jupyter Notebook is being used
# !pip install scikit-learn==1.2.2 seaborn==0.13.1 matplotlib==3.7.1 numpy==1.25.2 pandas==1.5.3 imblearn==0.12.0 xgboost==2.0.3 -q --user
# !pip install --upgrade -q threadpoolctl


df = pd.read_csv("/content/BankChurners.csv")


# Initial first rows
df.head()


# Initial last few rows
df.tail()


# Shape of dataset
df.shape

(10127, 21)


# Data types
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10127 entries, 0 to 10126
Data columns (total 21 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   CLIENTNUM                 10127 non-null  int64  
 1   Attrition_Flag            10127 non-null  object 
 2   Customer_Age              10127 non-null  int64  
 3   Gender                    10127 non-null  object 
 4   Dependent_count           10127 non-null  int64  
 5   Education_Level           8608 non-null   object 
 6   Marital_Status            9378 non-null   object 
 7   Income_Category           10127 non-null  object 
 8   Card_Category             10127 non-null  object 
 9   Months_on_book            10127 non-null  int64  
 10  Total_Relationship_Count  10127 non-null  int64  
 11  Months_Inactive_12_mon    10127 non-null  int64  
 12  Contacts_Count_12_mon     10127 non-null  int64  
 13  Credit_Limit              10127 non-null  float64
 14  Total_Revolving_Bal       10127 non-null  int64  
 15  Avg_Open_To_Buy           10127 non-null  float64
 16  Total_Amt_Chng_Q4_Q1      10127 non-null  float64
 17  Total_Trans_Amt           10127 non-null  int64  
 18  Total_Trans_Ct            10127 non-null  int64  
 19  Total_Ct_Chng_Q4_Q1       10127 non-null  float64
 20  Avg_Utilization_Ratio     10127 non-null  float64
dtypes: float64(5), int64(10), object(6)
memory usage: 1.6+ MB


# Misisng Values
df.isnull().sum()

CLIENTNUM                      0
Attrition_Flag                 0
Customer_Age                   0
Gender                         0
Dependent_count                0
Education_Level             1519
Marital_Status               749
Income_Category                0
Card_Category                  0
Months_on_book                 0
Total_Relationship_Count       0
Months_Inactive_12_mon         0
Contacts_Count_12_mon          0
Credit_Limit                   0
Total_Revolving_Bal            0
Avg_Open_To_Buy                0
Total_Amt_Chng_Q4_Q1           0
Total_Trans_Amt                0
Total_Trans_Ct                 0
Total_Ct_Chng_Q4_Q1            0
Avg_Utilization_Ratio          0
dtype: int64


round(df.isnull().sum() / df.isnull().count() * 100, 2)

CLIENTNUM                   0.000
Attrition_Flag              0.000
Customer_Age                0.000
Gender                      0.000
Dependent_count             0.000
Education_Level            15.000
Marital_Status              7.400
Income_Category             0.000
Card_Category               0.000
Months_on_book              0.000
Total_Relationship_Count    0.000
Months_Inactive_12_mon      0.000
Contacts_Count_12_mon       0.000
Credit_Limit                0.000
Total_Revolving_Bal         0.000
Avg_Open_To_Buy             0.000
Total_Amt_Chng_Q4_Q1        0.000
Total_Trans_Amt             0.000
Total_Trans_Ct              0.000
Total_Ct_Chng_Q4_Q1         0.000
Avg_Utilization_Ratio       0.000
dtype: float64


# Duplicates
df.duplicated().sum()

0


df.drop(["CLIENTNUM"], axis=1, inplace=True)


data = df.copy()


# Data summary
data.describe(include='all').T


data.describe()


# function to plot a boxplot and a histogram along the same scale.


def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (12,7))
    kde: whether to the show density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a triangle will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram


# function to create labeled barplots


def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 1, 5))
    else:
        plt.figure(figsize=(n + 1, 5))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n].sort_values(),
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot


# function to plot stacked bar chart

def stacked_barplot(data, predictor, target):
    """
    Print the category counts and plot a stacked bar chart

    data: dataframe
    predictor: independent variable
    target: target variable
    """
    count = data[predictor].nunique()
    sorter = data[target].value_counts().index[-1]
    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
        by=sorter, ascending=False
    )
    print(tab1)
    print("-" * 120)
    tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
        by=sorter, ascending=False
    )
    tab.plot(kind="bar", stacked=True, figsize=(count + 1, 5))
    plt.legend(
        loc="lower left", frameon=False,
    )
    plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    plt.show()


# Function to plot stacked bar chart with percentages
def stacked_barplot_perc(data, predictor, target):
    """
    Print the category counts and plot a stacked bar chart with percentages.

    data: DataFrame
    predictor: Independent variable (column name)
    target: Target variable (column name)
    """

    count = data[predictor].nunique()  # Number of unique categories in predictor

    # Sorting for stacked bar chart
    sorter = data[target].value_counts().index[-1]
    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(by=sorter, ascending=False)
    print(tab1)

    # Crosstab for percentage calculations
    tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(by=sorter, ascending=False)

    # Plotting
    tab.plot(kind="bar", stacked=True, figsize=(count + 1, 5))
    plt.xlabel(predictor, fontsize=12)
    plt.ylabel('Percentage', fontsize=12)
    plt.title(f'Proportion of {target} by {predictor}', fontsize=14)  # Add a descriptive title
    plt.legend(title=target, loc="upper left", bbox_to_anchor=(1, 1), frameon=False)

    # Add percentage labels inside bars
    for n, x in enumerate([*tab.index.values]):  # Iterate over each bar (predictor category)
        for (proportion, y_loc) in zip(tab.loc[x], tab.loc[x].cumsum()):
            if proportion != 0:
                plt.text(
                    x=n - 0.17,
                    y=(y_loc - proportion) + (proportion / 2),  # Center the text within the bar segment
                    s=f'{proportion:.1%}',  # Format as percentage with one decimal place
                    color="white",
                    fontsize=10,
                )

    plt.show()


### Function to plot distributions

def distribution_plot_wrt_target(data, predictor, target):

    fig, axs = plt.subplots(2, 2, figsize=(12, 10))

    target_uniq = data[target].unique()

    axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
    sns.histplot(
        data=data[data[target] == target_uniq[0]],
        x=predictor,
        kde=True,
        ax=axs[0, 0],
        color="teal",
    )

    axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
    sns.histplot(
        data=data[data[target] == target_uniq[1]],
        x=predictor,
        kde=True,
        ax=axs[0, 1],
        color="orange",
    )

    axs[1, 0].set_title("Boxplot w.r.t target")
    sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")

    axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
    sns.boxplot(
        data=data,
        x=target,
        y=predictor,
        ax=axs[1, 1],
        showfliers=False,
        palette="gist_rainbow",
    )

    plt.tight_layout()
    plt.show()


# Review data
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10127 entries, 0 to 10126
Data columns (total 20 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Attrition_Flag            10127 non-null  object 
 1   Customer_Age              10127 non-null  int64  
 2   Gender                    10127 non-null  object 
 3   Dependent_count           10127 non-null  int64  
 4   Education_Level           8608 non-null   object 
 5   Marital_Status            9378 non-null   object 
 6   Income_Category           10127 non-null  object 
 7   Card_Category             10127 non-null  object 
 8   Months_on_book            10127 non-null  int64  
 9   Total_Relationship_Count  10127 non-null  int64  
 10  Months_Inactive_12_mon    10127 non-null  int64  
 11  Contacts_Count_12_mon     10127 non-null  int64  
 12  Credit_Limit              10127 non-null  float64
 13  Total_Revolving_Bal       10127 non-null  int64  
 14  Avg_Open_To_Buy           10127 non-null  float64
 15  Total_Amt_Chng_Q4_Q1      10127 non-null  float64
 16  Total_Trans_Amt           10127 non-null  int64  
 17  Total_Trans_Ct            10127 non-null  int64  
 18  Total_Ct_Chng_Q4_Q1       10127 non-null  float64
 19  Avg_Utilization_Ratio     10127 non-null  float64
dtypes: float64(5), int64(9), object(6)
memory usage: 1.5+ MB


# Age
histogram_boxplot(data, 'Customer_Age')


# break down of age
labeled_barplot(data, 'Customer_Age', perc=True)


# Boxplot for Dependent count
histogram_boxplot(data, 'Dependent_count')


# dependent count breakdown
labeled_barplot(data, 'Dependent_count', perc = True)


# Months on book
histogram_boxplot(data, 'Months_on_book')


# break down on months on book
labeled_barplot(data, 'Months_on_book', perc=True)


## Observation on Total_Relationship_Count
histogram_boxplot(data, 'Total_Relationship_Count')


# breakdown by percentage
labeled_barplot(data, 'Total_Relationship_Count', perc=True)


# Observation on Months_Inactive_12_mon
histogram_boxplot(data, 'Months_Inactive_12_mon')


# Break down of Months
labeled_barplot(data, 'Months_Inactive_12_mon', perc=True)


# Observation Contacts_Count_12_mon
histogram_boxplot(data, 'Contacts_Count_12_mon')


# break down of contact
labeled_barplot(data, 'Contacts_Count_12_mon', perc=True)


# Observation on  Credit_Limit
histogram_boxplot(data, 'Credit_Limit')


# Breakdown of credit limit for customers
bin_edges = [0, 5000, 10000, 15000, 20000, 25000, 30000, 35000]
bin_labels = ['0-5k', '5k-10k', '10k-15k', '15k-20k', '20k-25k', '25k-30k', '30k+']

# Create a New Column for Bins
data['Credit_Limit_Bin'] = pd.cut(data['Credit_Limit'], bins=bin_edges, labels=bin_labels, right=False)

# Calculate Percentage per Bin and Sort
binned_data = data['Credit_Limit_Bin'].value_counts(normalize=True).mul(100).reset_index(name='Percentage').rename(columns={'index': 'Credit_Limit_Bin'}).sort_values(by='Credit_Limit_Bin')

# Plotting the Barplot
sns.set(style="whitegrid")
plt.figure(figsize=(10, 6))
sns.barplot(x='Credit_Limit_Bin', y='Percentage', data=binned_data)
plt.title('Distribution of Credit Limits', fontsize=14)
plt.xlabel('Credit Limit Range', fontsize=12)
plt.ylabel('Percentage of Customers (%)', fontsize=12)
plt.show()


# Observation on Total_Revolving_Bal
histogram_boxplot(data, 'Total_Revolving_Bal')


# Observation on Avg_Open_To_Buy
histogram_boxplot(data, 'Avg_Open_To_Buy')


# Pull up info
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10127 entries, 0 to 10126
Data columns (total 21 columns):
 #   Column                    Non-Null Count  Dtype   
---  ------                    --------------  -----   
 0   Attrition_Flag            10127 non-null  object  
 1   Customer_Age              10127 non-null  int64   
 2   Gender                    10127 non-null  object  
 3   Dependent_count           10127 non-null  int64   
 4   Education_Level           8608 non-null   object  
 5   Marital_Status            9378 non-null   object  
 6   Income_Category           10127 non-null  object  
 7   Card_Category             10127 non-null  object  
 8   Months_on_book            10127 non-null  int64   
 9   Total_Relationship_Count  10127 non-null  int64   
 10  Months_Inactive_12_mon    10127 non-null  int64   
 11  Contacts_Count_12_mon     10127 non-null  int64   
 12  Credit_Limit              10127 non-null  float64 
 13  Total_Revolving_Bal       10127 non-null  int64   
 14  Avg_Open_To_Buy           10127 non-null  float64 
 15  Total_Amt_Chng_Q4_Q1      10127 non-null  float64 
 16  Total_Trans_Amt           10127 non-null  int64   
 17  Total_Trans_Ct            10127 non-null  int64   
 18  Total_Ct_Chng_Q4_Q1       10127 non-null  float64 
 19  Avg_Utilization_Ratio     10127 non-null  float64 
 20  Credit_Limit_Bin          10127 non-null  category
dtypes: category(1), float64(5), int64(9), object(6)
memory usage: 1.6+ MB


# Observation on Total_Amt_Chng_Q4_Q1
histogram_boxplot(data, 'Total_Amt_Chng_Q4_Q1')


# Define Bin Edges
bin_edges = [0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]
bin_labels = ['0-0.5', '0.5-1.0', '1.0-1.5', '1.5-2.0', '2.0-2.5', '2.5-3.0']

# Create a New Column for Bins
data['Total_Amt_Chng_Q4_Q1_Binned'] = pd.cut(data['Total_Amt_Chng_Q4_Q1'], bins=bin_edges, labels=bin_labels)

# Plot the Histogram with Bins
plt.figure(figsize=(12, 6))
sns.histplot(data=data, x='Total_Amt_Chng_Q4_Q1_Binned', kde=False)  # Removed kde for clarity
plt.title('Distribution of Change in Transaction Amount (Q4 over Q1) - Binned', fontsize=14)
plt.xlabel('Change in Transaction Amount (Binned)', fontsize=12)
plt.ylabel('Count', fontsize=12)
plt.xticks(rotation=45)
plt.show()


# Obersvation on total transaction amount
histogram_boxplot(data, 'Total_Trans_Amt')


# Observation on Total_Trans_Ct
histogram_boxplot(data, 'Total_Trans_Ct')


# Observation on Total_Ct_Chng_Q4_Q1
histogram_boxplot(data, 'Total_Ct_Chng_Q4_Q1')


#Observation on Avg_Utilization_Ratio
histogram_boxplot(data, 'Avg_Utilization_Ratio')


# Pull up data
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10127 entries, 0 to 10126
Data columns (total 22 columns):
 #   Column                       Non-Null Count  Dtype   
---  ------                       --------------  -----   
 0   Attrition_Flag               10127 non-null  object  
 1   Customer_Age                 10127 non-null  int64   
 2   Gender                       10127 non-null  object  
 3   Dependent_count              10127 non-null  int64   
 4   Education_Level              8608 non-null   object  
 5   Marital_Status               9378 non-null   object  
 6   Income_Category              10127 non-null  object  
 7   Card_Category                10127 non-null  object  
 8   Months_on_book               10127 non-null  int64   
 9   Total_Relationship_Count     10127 non-null  int64   
 10  Months_Inactive_12_mon       10127 non-null  int64   
 11  Contacts_Count_12_mon        10127 non-null  int64   
 12  Credit_Limit                 10127 non-null  float64 
 13  Total_Revolving_Bal          10127 non-null  int64   
 14  Avg_Open_To_Buy              10127 non-null  float64 
 15  Total_Amt_Chng_Q4_Q1         10127 non-null  float64 
 16  Total_Trans_Amt              10127 non-null  int64   
 17  Total_Trans_Ct               10127 non-null  int64   
 18  Total_Ct_Chng_Q4_Q1          10127 non-null  float64 
 19  Avg_Utilization_Ratio        10127 non-null  float64 
 20  Credit_Limit_Bin             10127 non-null  category
 21  Total_Amt_Chng_Q4_Q1_Binned  10120 non-null  category
dtypes: category(2), float64(5), int64(9), object(6)
memory usage: 1.6+ MB


# Countplot for attrition Flag
labeled_barplot(data, 'Attrition_Flag', perc = True)


# Countplot for Gender
labeled_barplot(data, 'Gender', perc = True)


# Count plot for education level
labeled_barplot(data, 'Education_Level', perc = True)


data['Education_Level'].value_counts().sum()

8608


# Count plot for Marital status
labeled_barplot(data, 'Marital_Status', perc =True)


# Missing values count
data['Marital_Status'].value_counts().sum()

9378


# income category values
data['Income_Category'].unique()

array(['$60K - $80K', 'Less than $40K', '$80K - $120K', '$40K - $60K',
       '$120K +', 'abc'], dtype=object)


#Change abc to unknown for income category
data['Income_Category'] = data['Income_Category'].replace('abc', 'Unknown')


# Income category
labeled_barplot(data, 'Income_Category', perc = True);


#Coutnplot on card category
labeled_barplot(data, 'Card_Category', perc = True)


# Get only the numerical columns
numeric_columns = data.select_dtypes(include=['int64', 'float64'])

# Calculate the correlation matrix for the numerical columns
correlation_matrix = numeric_columns.corr()

# Create the heatmap
plt.figure(figsize=(15, 7))
sns.heatmap(correlation_matrix, annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral")
plt.title('Correlation Heatmap for Numerical Columns')
plt.show()


# Gender & target
stacked_barplot_perc(data, 'Gender', 'Attrition_Flag')

Attrition_Flag  Attrited Customer  Existing Customer    All
Gender                                                     
All                          1627               8500  10127
F                             930               4428   5358
M                             697               4072   4769


# Education level & target
stacked_barplot_perc(data, 'Education_Level','Attrition_Flag')

Attrition_Flag   Attrited Customer  Existing Customer   All
Education_Level                                            
All                           1371               7237  8608
Graduate                       487               2641  3128
High School                    306               1707  2013
Uneducated                     237               1250  1487
College                        154                859  1013
Doctorate                       95                356   451
Post-Graduate                   92                424   516


# marital status & target
stacked_barplot_perc(data, 'Marital_Status', 'Attrition_Flag')

Attrition_Flag  Attrited Customer  Existing Customer   All
Marital_Status                                            
All                          1498               7880  9378
Married                       709               3978  4687
Single                        668               3275  3943
Divorced                      121                627   748


# Income category and target
stacked_barplot_perc(data, 'Income_Category', 'Attrition_Flag')

Attrition_Flag   Attrited Customer  Existing Customer    All
Income_Category                                             
All                           1627               8500  10127
Less than $40K                 612               2949   3561
$40K - $60K                    271               1519   1790
$80K - $120K                   242               1293   1535
$60K - $80K                    189               1213   1402
Unknown                        187                925   1112
$120K +                        126                601    727


# Card category and target with
stacked_barplot_perc(data, 'Card_Category', 'Attrition_Flag')

Attrition_Flag  Attrited Customer  Existing Customer    All
Card_Category                                              
All                          1627               8500  10127
Blue                         1519               7917   9436
Silver                         82                473    555
Gold                           21                 95    116
Platinum                        5                 15     20


# Total_Relationship_Count & target
stacked_barplot_perc(data, 'Total_Relationship_Count', 'Attrition_Flag')

Attrition_Flag            Attrited Customer  Existing Customer    All
Total_Relationship_Count                                             
All                                    1627               8500  10127
3                                       400               1905   2305
2                                       346                897   1243
1                                       233                677    910
5                                       227               1664   1891
4                                       225               1687   1912
6                                       196               1670   1866


# Months_Inactive_12_mon & target
stacked_barplot_perc(data, 'Months_Inactive_12_mon', 'Attrition_Flag')

Attrition_Flag          Attrited Customer  Existing Customer    All
Months_Inactive_12_mon                                             
All                                  1627               8500  10127
3                                     826               3020   3846
2                                     505               2777   3282
4                                     130                305    435
1                                     100               2133   2233
5                                      32                146    178
6                                      19                105    124
0                                      15                 14     29


distribution_plot_wrt_target(data,'Months_Inactive_12_mon', 'Attrition_Flag')


# Contacts_Count_12_mon & target
stacked_barplot_perc(data, 'Contacts_Count_12_mon', 'Attrition_Flag')

Attrition_Flag         Attrited Customer  Existing Customer    All
Contacts_Count_12_mon                                             
All                                 1627               8500  10127
3                                    681               2699   3380
2                                    403               2824   3227
4                                    315               1077   1392
1                                    108               1391   1499
5                                     59                117    176
6                                     54                  0     54
0                                      7                392    399


# Customer age & Target
distribution_plot_wrt_target(data, 'Customer_Age', 'Attrition_Flag')


# Customers Age & target
stacked_barplot_perc(data, 'Customer_Age', 'Attrition_Flag')

Attrition_Flag  Attrited Customer  Existing Customer    All
Customer_Age                                               
All                          1627               8500  10127
43                             85                388    473
48                             85                387    472
44                             84                416    500
46                             82                408    490
45                             79                407    486
49                             79                416    495
47                             76                403    479
41                             76                303    379
50                             71                381    452
54                             69                238    307
40                             64                297    361
42                             62                364    426
53                             59                328    387
52                             58                318    376
51                             58                340    398
55                             51                228    279
39                             48                285    333
38                             47                256    303
56                             43                219    262
59                             40                117    157
37                             37                223    260
57                             33                190    223
58                             24                133    157
36                             24                197    221
35                             21                163    184
33                             20                107    127
34                             19                127    146
32                             17                 89    106
61                             17                 76     93
62                             17                 76     93
30                             15                 55     70
31                             13                 78     91
60                             13                114    127
65                              9                 92    101
63                              8                 57     65
29                              7                 49     56
26                              6                 72     78
64                              5                 38     43
27                              3                 29     32
28                              1                 28     29
66                              1                  1      2
68                              1                  1      2
67                              0                  4      4
70                              0                  1      1
73                              0                  1      1


# Depdendent count & target
stacked_barplot_perc(data, 'Dependent_count', 'Attrition_Flag')

Attrition_Flag   Attrited Customer  Existing Customer    All
Dependent_count                                             
All                           1627               8500  10127
3                              482               2250   2732
2                              417               2238   2655
1                              269               1569   1838
4                              260               1314   1574
0                              135                769    904
5                               64                360    424


# Months on Book & Target
distribution_plot_wrt_target(data, 'Months_on_book', 'Attrition_Flag')


# Months on book & Atrritubed
stacked_barplot_perc(data, 'Months_on_book', 'Attrition_Flag')

Attrition_Flag  Attrited Customer  Existing Customer    All
Months_on_book                                             
All                          1627               8500  10127
36                            430               2033   2463
39                             64                277    341
37                             62                296    358
30                             58                242    300
38                             57                290    347
34                             57                296    353
41                             51                246    297
33                             48                257    305
40                             45                288    333
35                             45                272    317
32                             44                245    289
28                             43                232    275
44                             42                188    230
43                             42                231    273
46                             36                161    197
42                             36                235    271
29                             34                207    241
31                             34                284    318
45                             33                194    227
25                             31                134    165
24                             28                132    160
48                             27                135    162
50                             25                 71     96
49                             24                117    141
26                             24                162    186
47                             24                147    171
27                             23                183    206
22                             20                 85    105
56                             17                 86    103
51                             16                 64     80
18                             13                 45     58
20                             13                 61     74
52                             12                 50     62
23                             12                104    116
21                             10                 73     83
15                              9                 25     34
53                              7                 71     78
13                              7                 63     70
19                              6                 57     63
54                              6                 47     53
17                              4                 35     39
55                              4                 38     42
16                              3                 26     29
14                              1                 15     16


# Credit Limit & target
distribution_plot_wrt_target(data, 'Credit_Limit', 'Attrition_Flag')


# Total revolving balance & target
distribution_plot_wrt_target(data, 'Total_Revolving_Bal', 'Attrition_Flag')


# Avg_Open_To_Buy & target
distribution_plot_wrt_target(data, 'Avg_Open_To_Buy', 'Attrition_Flag')


# Total_Amt_Chng_Q4_Q1 & target
distribution_plot_wrt_target(data, 'Total_Amt_Chng_Q4_Q1', 'Attrition_Flag')


# Total_Trans_Amt & target
distribution_plot_wrt_target(data, 'Total_Trans_Amt', 'Attrition_Flag')


# Total_Ct_Chng_Q4_Q1 & target
distribution_plot_wrt_target(data, 'Total_Ct_Chng_Q4_Q1', 'Attrition_Flag')


# Avg_Utilization_Ratio & target
distribution_plot_wrt_target(data, 'Avg_Utilization_Ratio', 'Attrition_Flag')


# outlier detection using boxplot
numeric_columns = data.select_dtypes(include=np.number).columns.tolist()


plt.figure(figsize=(15, 12))

for i, variable in enumerate(numeric_columns):
    plt.subplot(4, 4, i + 1)
    plt.boxplot(data[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()


df1 = data.copy()


# Drop Total_Amt_Chng_Q4_Q1_Binned column (it is not needed)
df1 = df1.drop(['Total_Amt_Chng_Q4_Q1_Binned'], axis=1)


# Drop Credit_Limit_Bin (it is not needed)
df1 = df1.drop(['Credit_Limit_Bin'], axis=1)


X = df1.drop(["Attrition_Flag"], axis=1)
y = df1["Attrition_Flag"]


# Splitting data into training, validation and test set:
# first we split data into 2 parts, say temporary and test

X_temp, X_test, y_temp, y_test = train_test_split(
    X, y, test_size=0.2, random_state=1, stratify=y
)

# then we split the temporary set into train and validation

X_train, X_val, y_train, y_val = train_test_split(
    X_temp, y_temp, test_size=0.25, random_state=1, stratify=y_temp
)
print(X_train.shape, X_val.shape, X_test.shape)

(6075, 19) (2026, 19) (2026, 19)


print("Number of rows in train data =", X_train.shape[0])
print("Number of rows in validation data =", X_val.shape[0])
print("Number of rows in test data =", X_test.shape[0])

Number of rows in train data = 6075
Number of rows in validation data = 2026
Number of rows in test data = 2026


df1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10127 entries, 0 to 10126
Data columns (total 20 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Attrition_Flag            10127 non-null  object 
 1   Customer_Age              10127 non-null  int64  
 2   Gender                    10127 non-null  object 
 3   Dependent_count           10127 non-null  int64  
 4   Education_Level           8608 non-null   object 
 5   Marital_Status            9378 non-null   object 
 6   Income_Category           10127 non-null  object 
 7   Card_Category             10127 non-null  object 
 8   Months_on_book            10127 non-null  int64  
 9   Total_Relationship_Count  10127 non-null  int64  
 10  Months_Inactive_12_mon    10127 non-null  int64  
 11  Contacts_Count_12_mon     10127 non-null  int64  
 12  Credit_Limit              10127 non-null  float64
 13  Total_Revolving_Bal       10127 non-null  int64  
 14  Avg_Open_To_Buy           10127 non-null  float64
 15  Total_Amt_Chng_Q4_Q1      10127 non-null  float64
 16  Total_Trans_Amt           10127 non-null  int64  
 17  Total_Trans_Ct            10127 non-null  int64  
 18  Total_Ct_Chng_Q4_Q1       10127 non-null  float64
 19  Avg_Utilization_Ratio     10127 non-null  float64
dtypes: float64(5), int64(9), object(6)
memory usage: 1.5+ MB


# Define a dictionary to map class labels to 1 or 0
attrition_map = {'Attrited Customer': 1, 'Existing Customer': 0}

# Apply the mapping and convert to integer
y_train = y_train.map(attrition_map).astype(int)
y_val = y_val.map(attrition_map).astype(int)
y_test = y_test.map(attrition_map).astype(int)


# Define a dictionary to map 'M' to 1 and 'F' to 0
gender_map = {'M': 1, 'F': 0}
# Apply the mapping and convert to numeric
data['Gender'] = data['Gender'].map(gender_map).astype(int)


# Checking columns for missing values in train, validation or test sets
print(X_train.isna().sum())
print("-" * 30)
print(X_val.isna().sum())
print("-" * 30)
print(X_test.isna().sum())

Customer_Age                  0
Gender                        0
Dependent_count               0
Education_Level             897
Marital_Status              432
Income_Category               0
Card_Category                 0
Months_on_book                0
Total_Relationship_Count      0
Months_Inactive_12_mon        0
Contacts_Count_12_mon         0
Credit_Limit                  0
Total_Revolving_Bal           0
Avg_Open_To_Buy               0
Total_Amt_Chng_Q4_Q1          0
Total_Trans_Amt               0
Total_Trans_Ct                0
Total_Ct_Chng_Q4_Q1           0
Avg_Utilization_Ratio         0
dtype: int64
------------------------------
Customer_Age                  0
Gender                        0
Dependent_count               0
Education_Level             308
Marital_Status              168
Income_Category               0
Card_Category                 0
Months_on_book                0
Total_Relationship_Count      0
Months_Inactive_12_mon        0
Contacts_Count_12_mon         0
Credit_Limit                  0
Total_Revolving_Bal           0
Avg_Open_To_Buy               0
Total_Amt_Chng_Q4_Q1          0
Total_Trans_Amt               0
Total_Trans_Ct                0
Total_Ct_Chng_Q4_Q1           0
Avg_Utilization_Ratio         0
dtype: int64
------------------------------
Customer_Age                  0
Gender                        0
Dependent_count               0
Education_Level             314
Marital_Status              149
Income_Category               0
Card_Category                 0
Months_on_book                0
Total_Relationship_Count      0
Months_Inactive_12_mon        0
Contacts_Count_12_mon         0
Credit_Limit                  0
Total_Revolving_Bal           0
Avg_Open_To_Buy               0
Total_Amt_Chng_Q4_Q1          0
Total_Trans_Amt               0
Total_Trans_Ct                0
Total_Ct_Chng_Q4_Q1           0
Avg_Utilization_Ratio         0
dtype: int64


import pandas as pd
from sklearn.impute import SimpleImputer

# Get list of categorical and numerical columns
cat_cols = list(X_train.select_dtypes(include='object').columns)
num_cols = list(X_train.select_dtypes(include=['int', 'float']).columns)

# Impute categorical columns
cat_imputer = SimpleImputer(strategy='most_frequent')
X_train[cat_cols] = cat_imputer.fit_transform(X_train[cat_cols])
X_val[cat_cols] = cat_imputer.transform(X_val[cat_cols])
X_test[cat_cols] = cat_imputer.transform(X_test[cat_cols])

# Impute numerical columns
num_imputer = SimpleImputer(strategy='mean')
X_train[num_cols] = num_imputer.fit_transform(X_train[num_cols])
X_val[num_cols] = num_imputer.transform(X_val[num_cols])
X_test[num_cols] = num_imputer.transform(X_test[num_cols])


# Checking that no column has missing values in train, validation or test sets
print(X_train.isna().sum())
print("-" * 30)
print(X_val.isna().sum())
print("-" * 30)
print(X_test.isna().sum())

Customer_Age                0
Gender                      0
Dependent_count             0
Education_Level             0
Marital_Status              0
Income_Category             0
Card_Category               0
Months_on_book              0
Total_Relationship_Count    0
Months_Inactive_12_mon      0
Contacts_Count_12_mon       0
Credit_Limit                0
Total_Revolving_Bal         0
Avg_Open_To_Buy             0
Total_Amt_Chng_Q4_Q1        0
Total_Trans_Amt             0
Total_Trans_Ct              0
Total_Ct_Chng_Q4_Q1         0
Avg_Utilization_Ratio       0
dtype: int64
------------------------------
Customer_Age                0
Gender                      0
Dependent_count             0
Education_Level             0
Marital_Status              0
Income_Category             0
Card_Category               0
Months_on_book              0
Total_Relationship_Count    0
Months_Inactive_12_mon      0
Contacts_Count_12_mon       0
Credit_Limit                0
Total_Revolving_Bal         0
Avg_Open_To_Buy             0
Total_Amt_Chng_Q4_Q1        0
Total_Trans_Amt             0
Total_Trans_Ct              0
Total_Ct_Chng_Q4_Q1         0
Avg_Utilization_Ratio       0
dtype: int64
------------------------------
Customer_Age                0
Gender                      0
Dependent_count             0
Education_Level             0
Marital_Status              0
Income_Category             0
Card_Category               0
Months_on_book              0
Total_Relationship_Count    0
Months_Inactive_12_mon      0
Contacts_Count_12_mon       0
Credit_Limit                0
Total_Revolving_Bal         0
Avg_Open_To_Buy             0
Total_Amt_Chng_Q4_Q1        0
Total_Trans_Amt             0
Total_Trans_Ct              0
Total_Ct_Chng_Q4_Q1         0
Avg_Utilization_Ratio       0
dtype: int64


cols = X_train.select_dtypes(include=["object", "category"])
for i in cols.columns:
    print(X_train[i].value_counts())
    print("*" * 30)

Gender
F    3205
M    2870
Name: count, dtype: int64
******************************
Education_Level
Graduate         2763
High School      1212
Uneducated        928
College           598
Post-Graduate     299
Doctorate         275
Name: count, dtype: int64
******************************
Marital_Status
Married     3280
Single      2346
Divorced     449
Name: count, dtype: int64
******************************
Income_Category
Less than $40K    2130
$40K - $60K       1076
$80K - $120K       918
$60K - $80K        847
Unknown            668
$120K +            436
Name: count, dtype: int64
******************************
Card_Category
Blue        5668
Silver       327
Gold          71
Platinum       9
Name: count, dtype: int64
******************************


cols = X_val.select_dtypes(include=["object", "category"])
for i in cols.columns:
    print(X_val[i].value_counts())
    print("*" * 30)

Gender
F    1106
M     920
Name: count, dtype: int64
******************************
Education_Level
Graduate         935
High School      399
Uneducated       278
College          217
Post-Graduate    108
Doctorate         89
Name: count, dtype: int64
******************************
Marital_Status
Married     1057
Single       814
Divorced     155
Name: count, dtype: int64
******************************
Income_Category
Less than $40K    712
$40K - $60K       340
$80K - $120K      306
$60K - $80K       280
Unknown           248
$120K +           140
Name: count, dtype: int64
******************************
Card_Category
Blue        1893
Silver       108
Gold          19
Platinum       6
Name: count, dtype: int64
******************************


cols = X_test.select_dtypes(include=["object", "category"])
for i in cols.columns:
    print(X_test[i].value_counts())
    print("*" * 30)

Gender
F    1047
M     979
Name: count, dtype: int64
******************************
Education_Level
Graduate         949
High School      402
Uneducated       281
College          198
Post-Graduate    109
Doctorate         87
Name: count, dtype: int64
******************************
Marital_Status
Married     1099
Single       783
Divorced     144
Name: count, dtype: int64
******************************
Income_Category
Less than $40K    719
$40K - $60K       374
$80K - $120K      311
$60K - $80K       275
Unknown           196
$120K +           151
Name: count, dtype: int64
******************************
Card_Category
Blue        1875
Silver       120
Gold          26
Platinum       5
Name: count, dtype: int64
******************************


X_train = pd.get_dummies(X_train, drop_first=True)
X_val = pd.get_dummies(X_val, drop_first=True)
X_test = pd.get_dummies(X_test, drop_first=True)
print(X_train.shape, X_val.shape, X_test.shape)

(6075, 30) (2026, 30) (2026, 30)


# defining a function to compute different metrics to check performance of a classification model built using sklearn
def model_performance_classification_sklearn(model, predictors, target):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    """

    # predicting using the independent variables
    pred = model.predict(predictors)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf


def confusion_matrix_sklearn(model, predictors, target):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    """
    y_pred = model.predict(predictors)
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")


models = []  # Empty list to store all the models

# Appending models into the list
models.append(("Bagging", BaggingClassifier(base_estimator=DecisionTreeClassifier(random_state=1, class_weight='balanced'), random_state=1)))
models.append(("Random forest", RandomForestClassifier(random_state=1, class_weight='balanced')))
models.append(("GBM", GradientBoostingClassifier(random_state=1)))
models.append(("Adaboost", AdaBoostClassifier(random_state=1)))
models.append(("dtree", DecisionTreeClassifier(random_state=1, class_weight='balanced')))

# Train and evaluate the models
print("\n" "Training Performance:" "\n")
for name, model in models:
    model.fit(X_train, y_train)
    scores_train = recall_score(y_train, model.predict(X_train))
    print("{}: {}".format(name, scores_train))

print("\n" "Validation Performance:" "\n")
for name, model in models:
    model.fit(X_train, y_train)
    scores_val = recall_score(y_val, model.predict(X_val))
    print("{}: {}".format(name, scores_val))

Training Performance:

Bagging: 0.9846311475409836
Random forest: 1.0
GBM: 0.9108606557377049
Adaboost: 0.8504098360655737
dtree: 1.0

Validation Performance:

Bagging: 0.7576687116564417
Random forest: 0.6625766871165644
GBM: 0.8190184049079755
Adaboost: 0.8128834355828221
dtree: 0.7300613496932515


# Print counts before oversampling
print("Before Oversampling, counts of label 'Attrited Customer': {}".format(sum(y_train == 1)))
print("Before Oversampling, counts of label 'Existing Customer': {} \n".format(sum(y_train == 0)))

# Synthetic Minority Over Sampling Technique
sm = SMOTE(sampling_strategy=1, k_neighbors=5, random_state=1)
X_train_over, y_train_over = sm.fit_resample(X_train, y_train)

# Print counts after oversampling
print("After Oversampling, counts of label 'Attrited Customer': {}".format(sum(y_train_over == 1)))
print("After Oversampling, counts of label 'Existing Customer': {} \n".format(sum(y_train_over == 0)))

print("After Oversampling, the shape of train_X: {}".format(X_train_over.shape))
print("After Oversampling, the shape of train_y: {} \n".format(y_train_over.shape))

Before Oversampling, counts of label 'Attrited Customer': 976
Before Oversampling, counts of label 'Existing Customer': 5099 

After Oversampling, counts of label 'Attrited Customer': 5099
After Oversampling, counts of label 'Existing Customer': 5099 

After Oversampling, the shape of train_X: (10198, 30)
After Oversampling, the shape of train_y: (10198,)


models = []  # Empty list to store all the models

# Appending models into the list
models.append(("Bagging", BaggingClassifier(base_estimator=DecisionTreeClassifier(random_state=1, class_weight='balanced'), random_state=1)))
models.append(("Random forest", RandomForestClassifier(random_state=1, class_weight='balanced')))
models.append(("GBM", GradientBoostingClassifier(random_state=1)))
models.append(("Adaboost", AdaBoostClassifier(random_state=1)))
models.append(("dtree", DecisionTreeClassifier(random_state=1, class_weight='balanced')))

# Training Performance
print("\n" "Training Performance:" "\n")
for name, model in models:
    model.fit(X_train_over, y_train_over)
    scores = recall_score(y_train_over, model.predict(X_train_over))  # Specify pos_label
    print("{}: {}".format(name, scores))

print("\n" "Validation Performance:" "\n")

for name, model in models:
    model.fit(X_train_over, y_train_over)
    scores_val = recall_score(y_val, model.predict(X_val))  # Specify pos_label
    print("{}: {}".format(name, scores_val))

Training Performance:

Bagging: 0.9972543636007061
Random forest: 1.0
GBM: 0.9847028829182193
Adaboost: 0.9731319866640518
dtree: 1.0

Validation Performance:

Bagging: 0.8282208588957055
Random forest: 0.7791411042944786
GBM: 0.8680981595092024
Adaboost: 0.8466257668711656
dtree: 0.803680981595092


# Print counts before undersampling
print("Before Undersampling, counts of label 'Attrited Customer': {}".format(sum(y_train == 1)))
print("Before Undersampling, counts of label 'Existing Customer': {} \n".format(sum(y_train == 0)))

# Random undersampler for under sampling the data
rus = RandomUnderSampler(random_state=1, sampling_strategy=1)
X_train_un, y_train_un = rus.fit_resample(X_train, y_train)

# Print counts after undersampling
print("After Undersampling, counts of label 'Attrited Customer': {}".format(sum(y_train_un == 1)))
print("After Undersampling, counts of label 'Existing Customer': {} \n".format(sum(y_train_un == 0)))

print("After Undersampling, the shape of train_X: {}".format(X_train_un.shape))
print("After Undersampling, the shape of train_y: {} \n".format(y_train_un.shape))

Before Undersampling, counts of label 'Attrited Customer': 976
Before Undersampling, counts of label 'Existing Customer': 5099 

After Undersampling, counts of label 'Attrited Customer': 976
After Undersampling, counts of label 'Existing Customer': 976 

After Undersampling, the shape of train_X: (1952, 30)
After Undersampling, the shape of train_y: (1952,)


from sklearn.metrics import recall_score

models = []  # Empty list to store all the models

# Appending models into the list
models.append(("Bagging", BaggingClassifier(base_estimator=DecisionTreeClassifier(random_state=1, class_weight='balanced'), random_state=1)))
models.append(("Random forest", RandomForestClassifier(random_state=1, class_weight='balanced')))
models.append(("GBM", GradientBoostingClassifier(random_state=1)))
models.append(("Adaboost", AdaBoostClassifier(random_state=1)))
models.append(("dtree", DecisionTreeClassifier(random_state=1, class_weight='balanced')))


print("\n" "Training Performance:" "\n")
for name, model in models:
    model.fit(X_train_un, y_train_un)
    scores = recall_score(y_train_un, model.predict(X_train_un))  # pos_label added
    print("{}: {}".format(name, scores))

print("\n" "Validation Performance:" "\n")

for name, model in models:
    model.fit(X_train_un, y_train_un)
    scores_val = recall_score(y_val, model.predict(X_val))  # pos_label added
    print("{}: {}".format(name, scores_val))

Training Performance:

Bagging: 0.9907786885245902
Random forest: 1.0
GBM: 0.9784836065573771
Adaboost: 0.951844262295082
dtree: 1.0

Validation Performance:

Bagging: 0.8650306748466258
Random forest: 0.9079754601226994
GBM: 0.9294478527607362
Adaboost: 0.9294478527607362
dtree: 0.8650306748466258


%%time
import sklearn.metrics as metrics
# defining model
Model = AdaBoostClassifier(random_state=1)

# Parameter grid to pass in RandomSearchCV
param_grid = {
    "n_estimators": np.arange(10, 40, 10),
    "learning_rate": [0.1, 0.01, 0.2, 0.05, 1],
    "base_estimator": [
        DecisionTreeClassifier(max_depth=1, random_state=1),
        DecisionTreeClassifier(max_depth=2, random_state=1),
        DecisionTreeClassifier(max_depth=3, random_state=1),
    ],
}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

#Calling RandomizedSearchCV
randomized_cv = RandomizedSearchCV(estimator=Model, param_distributions=param_grid, n_jobs = -1, n_iter=50, scoring=scorer, cv=5, random_state=1)

#Fitting parameters in RandomizedSearchCV
randomized_cv.fit(X_train_un, y_train_un)

print("Best parameters are {} with CV score={}:" .format(randomized_cv.best_params_,randomized_cv.best_score_))

Best parameters are {'n_estimators': 30, 'learning_rate': 0.1, 'base_estimator': DecisionTreeClassifier(max_depth=3, random_state=1)} with CV score=0.9487650444793303:
CPU times: user 1.55 s, sys: 146 ms, total: 1.69 s
Wall time: 30.9 s


tuned_adb = AdaBoostClassifier(
    random_state=1,
    n_estimators=30,
    learning_rate=0.1,
    base_estimator=DecisionTreeClassifier(max_depth=3, random_state=1),
)
tuned_adb.fit(X_train_un, y_train_un)

AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=3,
                                                         random_state=1),
                   learning_rate=0.1, n_estimators=30, random_state=1)

AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=3,
                                                         random_state=1),
                   learning_rate=0.1, n_estimators=30, random_state=1)

DecisionTreeClassifier(max_depth=3, random_state=1)

DecisionTreeClassifier(max_depth=3, random_state=1)


# Checking model's performance on training set
adb_train = model_performance_classification_sklearn(tuned_adb, X_train_un, y_train_un)
adb_train


# Checking model's performance on validation set
adb_val = model_performance_classification_sklearn(tuned_adb, X_val, y_val)
adb_val


%%time

#Creating pipeline
Model = GradientBoostingClassifier(random_state=1)

#Parameter grid to pass in RandomSearchCV
param_grid = {
    "init": [AdaBoostClassifier(random_state=1),DecisionTreeClassifier(random_state=1)],
    "n_estimators": np.arange(125,175,25),
    "learning_rate": [0.01, 0.2, 0.05, 1],
    "subsample":[0.8,0.9,1],
    "max_features":[0.5,0.7,1],
}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

#Calling RandomizedSearchCV
randomized_cv = RandomizedSearchCV(estimator=Model, param_distributions=param_grid, n_iter=50, scoring=scorer, cv=5, random_state=1, n_jobs = -1)

#Fitting parameters in RandomizedSearchCV
randomized_cv.fit(X_train_un,y_train_un)

print("Best parameters are {} with CV score={}:" .format(randomized_cv.best_params_,randomized_cv.best_score_))

Best parameters are {'subsample': 0.9, 'n_estimators': 150, 'max_features': 0.5, 'learning_rate': 0.05, 'init': AdaBoostClassifier(random_state=1)} with CV score=0.9538932496075354:
CPU times: user 2.96 s, sys: 251 ms, total: 3.21 s
Wall time: 2min 8s


tuned_gbm1 = GradientBoostingClassifier(
    random_state=1,
    subsample=0.9,
    n_estimators=150,
    max_features=0.5,
    learning_rate=0.05,
    init=AdaBoostClassifier(random_state=1),
)
tuned_gbm1.fit(X_train_un, y_train_un)

GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),
                           learning_rate=0.05, max_features=0.5,
                           n_estimators=150, random_state=1, subsample=0.9)

GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),
                           learning_rate=0.05, max_features=0.5,
                           n_estimators=150, random_state=1, subsample=0.9)

AdaBoostClassifier(random_state=1)

AdaBoostClassifier(random_state=1)


# Checking model's performance on training set
gbm1_train = model_performance_classification_sklearn(
    tuned_gbm1, X_train_un, y_train_un
)
gbm1_train


# Checking model's performance on validation set
gbm1_val = model_performance_classification_sklearn(tuned_gbm1, X_val, y_val)
gbm1_val


%%time

#defining model
Model = GradientBoostingClassifier(random_state=1)

#Parameter grid to pass in RandomSearchCV
param_grid = {
    "init": [AdaBoostClassifier(random_state=1),DecisionTreeClassifier(random_state=1)],
    "n_estimators": np.arange(75,150,25),
    "learning_rate": [0.1, 0.01, 0.2, 0.05, 1],
    "subsample":[0.5,0.7,1],
    "max_features":[0.5,0.7,1],
}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

#Calling RandomizedSearchCV
randomized_cv = RandomizedSearchCV(estimator=Model, param_distributions=param_grid, n_iter=50, scoring=scorer, cv=5, random_state=1, n_jobs = -1)

#Fitting parameters in RandomizedSearchCV
randomized_cv.fit(X_train_over, y_train_over)

print("Best parameters are {} with CV score={}:" .format(randomized_cv.best_params_,randomized_cv.best_score_))

Best parameters are {'subsample': 0.5, 'n_estimators': 100, 'max_features': 0.7, 'learning_rate': 0.01, 'init': AdaBoostClassifier(random_state=1)} with CV score=0.9541109122746253:
CPU times: user 6.86 s, sys: 659 ms, total: 7.52 s
Wall time: 6min 44s


tuned_gbm2 = GradientBoostingClassifier(
    random_state=1,
    subsample=0.5,
    n_estimators=100,
    max_features=0.7,
    learning_rate=0.01,
    init=AdaBoostClassifier(random_state=1),
)
tuned_gbm2.fit(X_train_over, y_train_over)

GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),
                           learning_rate=0.01, max_features=0.7, random_state=1,
                           subsample=0.5)

GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),
                           learning_rate=0.01, max_features=0.7, random_state=1,
                           subsample=0.5)

AdaBoostClassifier(random_state=1)

AdaBoostClassifier(random_state=1)


# Checking model's performance on training set
gbm2_train = model_performance_classification_sklearn(tuned_gbm2, X_train_over, y_train_over)
gbm2_train


# Checking model's performance on validation set
gbm2_val = model_performance_classification_sklearn(tuned_gbm2, X_val, y_val)
gbm2_val


# training performance comparison

models_train_comp_df = pd.concat(
    [
        gbm1_train.T,
        gbm2_train.T,
        adb_train.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Gradient boosting trained with Undersampled data",
    "Gradient boosting trained with Oversampled data",
    "AdaBoost trained with Undersampled data",
]
print("Training performance comparison:")
models_train_comp_df

Training performance comparison:


# Validation performance comparison

models_train_comp_df = pd.concat(
    [ gbm1_val.T, gbm2_val.T, adb_val.T], axis=1,
)
models_train_comp_df.columns = [
    "Gradient boosting trained with Undersampled data",
    "Gradient boosting trained with Oversampled data",
    "AdaBoost trained with Undersampled data",
]
print("Validation performance comparison:")
models_train_comp_df

Validation performance comparison:


# Checking model's performance on test set
tuned_adb_test = model_performance_classification_sklearn(tuned_adb, X_test, y_test)
tuned_adb_test


feature_names = X_train.columns
importances = tuned_adb.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


#Pull up info
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10127 entries, 0 to 10126
Data columns (total 20 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Attrition_Flag            10127 non-null  object 
 1   Customer_Age              10127 non-null  int64  
 2   Gender                    10127 non-null  object 
 3   Dependent_count           10127 non-null  int64  
 4   Education_Level           8608 non-null   object 
 5   Marital_Status            9378 non-null   object 
 6   Income_Category           10127 non-null  object 
 7   Card_Category             10127 non-null  object 
 8   Months_on_book            10127 non-null  int64  
 9   Total_Relationship_Count  10127 non-null  int64  
 10  Months_Inactive_12_mon    10127 non-null  int64  
 11  Contacts_Count_12_mon     10127 non-null  int64  
 12  Credit_Limit              10127 non-null  float64
 13  Total_Revolving_Bal       10127 non-null  int64  
 14  Avg_Open_To_Buy           10127 non-null  float64
 15  Total_Amt_Chng_Q4_Q1      10127 non-null  float64
 16  Total_Trans_Amt           10127 non-null  int64  
 17  Total_Trans_Ct            10127 non-null  int64  
 18  Total_Ct_Chng_Q4_Q1       10127 non-null  float64
 19  Avg_Utilization_Ratio     10127 non-null  float64
dtypes: float64(5), int64(9), object(6)
memory usage: 1.5+ MB


sns.pairplot(data=df, diag_kind="kde")
plt.show()


# Boxplot age & Education
plt.figure(figsize=(12, 6))
sns.boxplot(x='Education_Level', y='Customer_Age', data=df)
plt.xticks(rotation=45)
plt.show()


# Boxplot age & Card category
plt.figure(figsize=(12, 6))
sns.boxplot(x='Customer_Age', y='Card_Category', data=df)
plt.xticks(rotation=45)
plt.show()


# Total_Trans_Amt & Education
plt.figure(figsize=(12, 6))
sns.boxplot(x='Education_Level', y='Total_Trans_Amt', data=df)
plt.xticks(rotation=45)
plt.show()


# Marital Status & Total_Trans_Amt
plt.figure(figsize=(12, 6))
sns.boxplot(x='Marital_Status', y='Total_Trans_Amt', data=df)
plt.xticks(rotation=45)
plt.show()


# Age & Total_Ct_Chng_Q4_Q1
plt.figure(figsize=(12, 6))
sns.boxplot(x='Customer_Age', y='Total_Ct_Chng_Q4_Q1', data=df)
plt.xticks(rotation=45)
plt.show()


# Age & Total_Trans_Amt
plt.figure(figsize=(12, 6))
sns.boxplot(x='Customer_Age', y='Total_Trans_Amt', data=df)
plt.xticks(rotation=45)
plt.show()


# Convert ipynb to HTML


!jupyter nbconvert --to html /content/AML_Project_LearnerNotebook_FullCode_Final.ipynb

[NbConvertApp] WARNING | pattern '/content/AML_Project_LearnerNotebook_FullCode_Final.ipynb' matched no files
This application is used to convert notebook files (*.ipynb)
        to various other formats.

        WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.

Options
=======
The options below are convenience aliases to configurable class-options,
as listed in the "Equivalent to" description-line of the aliases.
To see all configurable class-options for some <cmd>, use:
    <cmd> --help-all

--debug
    set log level to logging.DEBUG (maximize logging output)
    Equivalent to: [--Application.log_level=10]
--show-config
    Show the application's configuration (human-readable format)
    Equivalent to: [--Application.show_config=True]
--show-config-json
    Show the application's configuration (json format)
    Equivalent to: [--Application.show_config_json=True]
--generate-config
    generate default config file
    Equivalent to: [--JupyterApp.generate_config=True]
-y
    Answer yes to any questions instead of prompting.
    Equivalent to: [--JupyterApp.answer_yes=True]
--execute
    Execute the notebook prior to export.
    Equivalent to: [--ExecutePreprocessor.enabled=True]
--allow-errors
    Continue notebook execution even if one of the cells throws an error and include the error message in the cell output (the default behaviour is to abort conversion). This flag is only relevant if '--execute' was specified, too.
    Equivalent to: [--ExecutePreprocessor.allow_errors=True]
--stdin
    read a single notebook file from stdin. Write the resulting notebook with default basename 'notebook.*'
    Equivalent to: [--NbConvertApp.from_stdin=True]
--stdout
    Write notebook output to stdout instead of files.
    Equivalent to: [--NbConvertApp.writer_class=StdoutWriter]
--inplace
    Run nbconvert in place, overwriting the existing notebook (only
            relevant when converting to notebook format)
    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory=]
--clear-output
    Clear output of current file and save in place,
            overwriting the existing notebook.
    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --ClearOutputPreprocessor.enabled=True]
--no-prompt
    Exclude input and output prompts from converted document.
    Equivalent to: [--TemplateExporter.exclude_input_prompt=True --TemplateExporter.exclude_output_prompt=True]
--no-input
    Exclude input cells and output prompts from converted document.
            This mode is ideal for generating code-free reports.
    Equivalent to: [--TemplateExporter.exclude_output_prompt=True --TemplateExporter.exclude_input=True --TemplateExporter.exclude_input_prompt=True]
--allow-chromium-download
    Whether to allow downloading chromium if no suitable version is found on the system.
    Equivalent to: [--WebPDFExporter.allow_chromium_download=True]
--disable-chromium-sandbox
    Disable chromium security sandbox when converting to PDF..
    Equivalent to: [--WebPDFExporter.disable_sandbox=True]
--show-input
    Shows code input. This flag is only useful for dejavu users.
    Equivalent to: [--TemplateExporter.exclude_input=False]
--embed-images
    Embed the images as base64 dataurls in the output. This flag is only useful for the HTML/WebPDF/Slides exports.
    Equivalent to: [--HTMLExporter.embed_images=True]
--sanitize-html
    Whether the HTML in Markdown cells and cell outputs should be sanitized..
    Equivalent to: [--HTMLExporter.sanitize_html=True]
--log-level=<Enum>
    Set the log level by value or name.
    Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']
    Default: 30
    Equivalent to: [--Application.log_level]
--config=<Unicode>
    Full path of a config file.
    Default: ''
    Equivalent to: [--JupyterApp.config_file]
--to=<Unicode>
    The export format to be used, either one of the built-in formats
            ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf']
            or a dotted object name that represents the import path for an
            ``Exporter`` class
    Default: ''
    Equivalent to: [--NbConvertApp.export_format]
--template=<Unicode>
    Name of the template to use
    Default: ''
    Equivalent to: [--TemplateExporter.template_name]
--template-file=<Unicode>
    Name of the template file to use
    Default: None
    Equivalent to: [--TemplateExporter.template_file]
--theme=<Unicode>
    Template specific theme(e.g. the name of a JupyterLab CSS theme distributed
    as prebuilt extension for the lab template)
    Default: 'light'
    Equivalent to: [--HTMLExporter.theme]
--sanitize_html=<Bool>
    Whether the HTML in Markdown cells and cell outputs should be sanitized.This
    should be set to True by nbviewer or similar tools.
    Default: False
    Equivalent to: [--HTMLExporter.sanitize_html]
--writer=<DottedObjectName>
    Writer class used to write the
                                        results of the conversion
    Default: 'FilesWriter'
    Equivalent to: [--NbConvertApp.writer_class]
--post=<DottedOrNone>
    PostProcessor class used to write the
                                        results of the conversion
    Default: ''
    Equivalent to: [--NbConvertApp.postprocessor_class]
--output=<Unicode>
    overwrite base name use for output files.
                can only be used when converting one notebook at a time.
    Default: ''
    Equivalent to: [--NbConvertApp.output_base]
--output-dir=<Unicode>
    Directory to write output(s) to. Defaults
                                  to output to the directory of each notebook. To recover
                                  previous default behaviour (outputting to the current
                                  working directory) use . as the flag value.
    Default: ''
    Equivalent to: [--FilesWriter.build_directory]
--reveal-prefix=<Unicode>
    The URL prefix for reveal.js (version 3.x).
            This defaults to the reveal CDN, but can be any url pointing to a copy
            of reveal.js.
            For speaker notes to work, this must be a relative path to a local
            copy of reveal.js: e.g., "reveal.js".
            If a relative path is given, it must be a subdirectory of the
            current directory (from which the server is run).
            See the usage documentation
            (https://nbconvert.readthedocs.io/en/latest/usage.html#reveal-js-html-slideshow)
            for more details.
    Default: ''
    Equivalent to: [--SlidesExporter.reveal_url_prefix]
--nbformat=<Enum>
    The nbformat version to write.
            Use this to downgrade notebooks.
    Choices: any of [1, 2, 3, 4]
    Default: 4
    Equivalent to: [--NotebookExporter.nbformat_version]

Examples
--------

    The simplest way to use nbconvert is

            > jupyter nbconvert mynotebook.ipynb --to html

            Options include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf'].

            > jupyter nbconvert --to latex mynotebook.ipynb

            Both HTML and LaTeX support multiple output templates. LaTeX includes
            'base', 'article' and 'report'.  HTML includes 'basic', 'lab' and
            'classic'. You can specify the flavor of the format used.

            > jupyter nbconvert --to html --template lab mynotebook.ipynb

            You can also pipe the output to stdout, rather than a file

            > jupyter nbconvert mynotebook.ipynb --stdout

            PDF is generated via latex

            > jupyter nbconvert mynotebook.ipynb --to pdf

            You can get (and serve) a Reveal.js-powered slideshow

            > jupyter nbconvert myslides.ipynb --to slides --post serve

            Multiple notebooks can be given at the command line in a couple of
            different ways:

            > jupyter nbconvert notebook*.ipynb
            > jupyter nbconvert notebook1.ipynb notebook2.ipynb

            or you can specify the notebooks list in a config file, containing::

                c.NbConvertApp.notebooks = ["my_notebook.ipynb"]

            > jupyter nbconvert --config mycfg.py

To see all available configurables, use `--help-all`.

	count	unique	top	freq	mean	std	min	25%	50%	75%	max
Attrition_Flag	10127	2	Existing Customer	8500	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Customer_Age	10127.000	NaN	NaN	NaN	46.326	8.017	26.000	41.000	46.000	52.000	73.000
Gender	10127	2	F	5358	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Dependent_count	10127.000	NaN	NaN	NaN	2.346	1.299	0.000	1.000	2.000	3.000	5.000
Education_Level	8608	6	Graduate	3128	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Marital_Status	9378	3	Married	4687	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Income_Category	10127	6	Less than $40K	3561	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Card_Category	10127	4	Blue	9436	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Months_on_book	10127.000	NaN	NaN	NaN	35.928	7.986	13.000	31.000	36.000	40.000	56.000
Total_Relationship_Count	10127.000	NaN	NaN	NaN	3.813	1.554	1.000	3.000	4.000	5.000	6.000
Months_Inactive_12_mon	10127.000	NaN	NaN	NaN	2.341	1.011	0.000	2.000	2.000	3.000	6.000
Contacts_Count_12_mon	10127.000	NaN	NaN	NaN	2.455	1.106	0.000	2.000	2.000	3.000	6.000
Credit_Limit	10127.000	NaN	NaN	NaN	8631.954	9088.777	1438.300	2555.000	4549.000	11067.500	34516.000
Total_Revolving_Bal	10127.000	NaN	NaN	NaN	1162.814	814.987	0.000	359.000	1276.000	1784.000	2517.000
Avg_Open_To_Buy	10127.000	NaN	NaN	NaN	7469.140	9090.685	3.000	1324.500	3474.000	9859.000	34516.000
Total_Amt_Chng_Q4_Q1	10127.000	NaN	NaN	NaN	0.760	0.219	0.000	0.631	0.736	0.859	3.397
Total_Trans_Amt	10127.000	NaN	NaN	NaN	4404.086	3397.129	510.000	2155.500	3899.000	4741.000	18484.000
Total_Trans_Ct	10127.000	NaN	NaN	NaN	64.859	23.473	10.000	45.000	67.000	81.000	139.000
Total_Ct_Chng_Q4_Q1	10127.000	NaN	NaN	NaN	0.712	0.238	0.000	0.582	0.702	0.818	3.714
Avg_Utilization_Ratio	10127.000	NaN	NaN	NaN	0.275	0.276	0.000	0.023	0.176	0.503	0.999

	Customer_Age	Dependent_count	Months_on_book	Total_Relationship_Count	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio
count	10127.000	10127.000	10127.000	10127.000	10127.000	10127.000	10127.000	10127.000	10127.000	10127.000	10127.000	10127.000	10127.000	10127.000
mean	46.326	2.346	35.928	3.813	2.341	2.455	8631.954	1162.814	7469.140	0.760	4404.086	64.859	0.712	0.275
std	8.017	1.299	7.986	1.554	1.011	1.106	9088.777	814.987	9090.685	0.219	3397.129	23.473	0.238	0.276
min	26.000	0.000	13.000	1.000	0.000	0.000	1438.300	0.000	3.000	0.000	510.000	10.000	0.000	0.000
25%	41.000	1.000	31.000	3.000	2.000	2.000	2555.000	359.000	1324.500	0.631	2155.500	45.000	0.582	0.023
50%	46.000	2.000	36.000	4.000	2.000	2.000	4549.000	1276.000	3474.000	0.736	3899.000	67.000	0.702	0.176
75%	52.000	3.000	40.000	5.000	3.000	3.000	11067.500	1784.000	9859.000	0.859	4741.000	81.000	0.818	0.503
max	73.000	5.000	56.000	6.000	6.000	6.000	34516.000	2517.000	34516.000	3.397	18484.000	139.000	3.714	0.999

	Gradient boosting trained with Undersampled data	Gradient boosting trained with Oversampled data	AdaBoost trained with Undersampled data
Accuracy	0.966	0.934	0.953
Recall	0.975	0.963	0.964
Precision	0.958	0.909	0.943
F1	0.966	0.935	0.953

	Gradient boosting trained with Undersampled data	Gradient boosting trained with Oversampled data	AdaBoost trained with Undersampled data
Accuracy	0.942	0.914	0.927
Recall	0.914	0.899	0.933
Precision	0.770	0.674	0.709
F1	0.836	0.770	0.805

	CLIENTNUM	Attrition_Flag	Customer_Age	Gender	Dependent_count	Education_Level	Marital_Status	Income_Category	Card_Category	Months_on_book	...	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio
0	768805383	Existing Customer	45	M	3	High School	Married	$60K - $80K	Blue	39	...	1	3	12691.000	777	11914.000	1.335	1144	42	1.625	0.061
1	818770008	Existing Customer	49	F	5	Graduate	Single	Less than $40K	Blue	44	...	1	2	8256.000	864	7392.000	1.541	1291	33	3.714	0.105
2	713982108	Existing Customer	51	M	3	Graduate	Married	$80K - $120K	Blue	36	...	1	0	3418.000	0	3418.000	2.594	1887	20	2.333	0.000
3	769911858	Existing Customer	40	F	4	High School	NaN	Less than $40K	Blue	34	...	4	1	3313.000	2517	796.000	1.405	1171	20	2.333	0.760
4	709106358	Existing Customer	40	M	3	Uneducated	Married	$60K - $80K	Blue	21	...	1	0	4716.000	0	4716.000	2.175	816	28	2.500	0.000

	CLIENTNUM	Attrition_Flag	Customer_Age	Gender	Dependent_count	Education_Level	Marital_Status	Income_Category	Card_Category	Months_on_book	...	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio
10122	772366833	Existing Customer	50	M	2	Graduate	Single	$40K - $60K	Blue	40	...	2	3	4003.000	1851	2152.000	0.703	15476	117	0.857	0.462
10123	710638233	Attrited Customer	41	M	2	NaN	Divorced	$40K - $60K	Blue	25	...	2	3	4277.000	2186	2091.000	0.804	8764	69	0.683	0.511
10124	716506083	Attrited Customer	44	F	1	High School	Married	Less than $40K	Blue	36	...	3	4	5409.000	0	5409.000	0.819	10291	60	0.818	0.000
10125	717406983	Attrited Customer	30	M	2	Graduate	NaN	$40K - $60K	Blue	36	...	3	3	5281.000	0	5281.000	0.535	8395	62	0.722	0.000
10126	714337233	Attrited Customer	43	F	2	Graduate	Married	Less than $40K	Silver	25	...	2	4	10388.000	1961	8427.000	0.703	10294	61	0.649	0.189

Credit Card Users Churn Prediction¶

Problem Statement¶

Business Context¶

Data Description¶

What Is a Revolving Balance?¶

What is the Average Open to buy?¶

What is the Average utilization Ratio?¶

Relation b/w Avg_Open_To_Buy, Credit_Limit and Avg_Utilization_Ratio:¶

Step 0 - Domain Knowledge & Initial Hypothesis¶

Importing necessary libraries¶

Loading the dataset¶

Data Overview¶

Missing values & Duplicates¶

Copy the Data¶

Data 5-Point Summary¶

Categorical data¶

Numeric data¶

Key Insights and Further Analysis:¶

Exploratory Data Analysis (EDA)¶

Functions need to be defined to carry out the Exploratory Data Analysis.¶

Univariate Analysis¶

Numeric¶

Observation on CLIENTNUM¶

Observation on Age¶

Observation on dependent count¶

Observation on Months on book¶

Observation on Total_Relationship_Count¶

Observation on Months_Inactive_12_mon¶

Observation on Contract Count 12 months¶

Observation on Credit_Limit¶

Observation on Total_Revolving_Bal¶

Observation on Avg_Open_To_Buy¶

Observation on Total_Amt_Chng_Q4_Q1¶

Observation on total transaction amount¶

Observation on Total_Trans_Ct¶

Observation on Total_Ct_Chng_Q4_Q1¶

Observation on Avg_Utilization_Ratio¶

Categorical (Univariate Analysis)¶

Observation on Attrition Flag¶

Observation on Gender¶

Observation on Education Level¶

Observation for Marital Status¶

Observation on Income Category¶

Observation for Card Category¶

Bivariate Analysis¶

Factors to explore in relation to target¶

Overview of Numeric columns¶

Demographics & Target¶

Insight¶

Observation on Income category & Target¶

Relationship & Target¶

Observation on Contacts Count 12 month¶

Numerical Predictors & Target¶

Observation on Customer Age & target¶

Observation on Dependent Count & Target¶

Observation on Months on Book & target¶

Credit Card Usage¶

Observation on Credit Limit¶

Observation on total revolving Balance & Target¶

Observation on Avg_Open_To_Buy & target¶

Observation on Total_Amt_Chng_Q4_Q1 & target¶

Total_Trans_Amt & target¶

Total_Ct_Chng_Q4_Q1 & target¶

Avg_Utilization_Ratio & target¶

Summary of EDA¶

Data Pre-processing¶

Split the Data into training, validation and test set¶

Missing values¶

Missing Value Treatment¶

Train Dataset¶

Validation Dataset¶

Test Dataset¶

Create Dummy Variables¶

Model Building¶

Functions for Models¶

Model evaluation criterion¶

Initial Building model¶

Model Building with original data¶

Model Building with Oversampled data¶

Model Building with Undersampled data¶